| other,5-5-E06-1035,ak | </term> . Examination of the effect of <term> | features | </term> shows that predicting top-level | #11468 Examination of the effect of features shows that predicting top-level and predicting subtopic boundaries are two distinct tasks: (1) for predicting subtopic boundaries, the lexical cohesion-based approach alone can achieve competitive results, (2) for predicting top-level boundaries, the machine learning approach that combines lexical-cohesion and conversational features performs best, and (3) conversational cues, such as cue phrases and overlapping speech, are better indicators for the top-level prediction task. | |
| model,16-6-E06-1035,ak | output </term> have a negative impact on <term> | models | </term> that combine <term> lexical-cohesion | #11561 We also find that the transcription errors inevitable in ASR output have a negative impact on models that combine lexical-cohesion and conversational features, but do not change the general preference of approach for the two tasks. |
